{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from findatapy.market import Market, MarketDataRequest, MarketDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from findatapy.util.dataconstants import DataConstants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Yahoo data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "market = Market(market_data_generator=MarketDataGenerator())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "md_request = MarketDataRequest(\n",
    "        start_date=\"decade\",            # start date\n",
    "        data_source='yahoo',     # use Bloomberg as data source\n",
    "        tickers=['Apple'], # ticker (findatapy)\n",
    "        fields=['close'],               # which fields to download\n",
    "        vendor_tickers=['aapl'],   # ticker (Alpha Vantage)\n",
    "        vendor_fields=['Close'])        # which Bloomberg fields to download)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-11-02 12:24:46,873 - findatapy.market.datavendorweb - INFO - Request Yahoo data\n",
      "2019-11-02 12:24:47,134 - numexpr.utils - INFO - NumExpr defaulting to 8 threads.\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "2019-11-02 12:24:47,347 - findatapy.market.datavendorweb - INFO - Completed request from Yahoo.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dkn22/Documents/Git/findatapy/findatapy/market/ioengine.py:264: FutureWarning: to_msgpack is deprecated and will be removed in a future version.\n",
      "It is recommended to use pyarrow for on-the-wire transmission of pandas objects.\n",
      "  r.set(fname, data_frame.to_msgpack(compress='blosc'))\n"
     ]
    }
   ],
   "source": [
    "df = market.fetch_market(md_request)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Apple.close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2009-11-05</td>\n",
       "      <td>27.718571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2009-11-06</td>\n",
       "      <td>27.762857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2009-11-09</td>\n",
       "      <td>28.780001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2009-11-10</td>\n",
       "      <td>28.997143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2009-11-11</td>\n",
       "      <td>29.035715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-10-28</td>\n",
       "      <td>249.050003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-10-29</td>\n",
       "      <td>243.289993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-10-30</td>\n",
       "      <td>243.259995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-10-31</td>\n",
       "      <td>248.759995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>255.820007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2515 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Apple.close\n",
       "Date                   \n",
       "2009-11-05    27.718571\n",
       "2009-11-06    27.762857\n",
       "2009-11-09    28.780001\n",
       "2009-11-10    28.997143\n",
       "2009-11-11    29.035715\n",
       "...                 ...\n",
       "2019-10-28   249.050003\n",
       "2019-10-29   243.289993\n",
       "2019-10-30   243.259995\n",
       "2019-10-31   248.759995\n",
       "2019-11-01   255.820007\n",
       "\n",
       "[2515 rows x 1 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a24919fd0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Apple.close'].pct_change().rolling(30).mean().plot(grid=True, figsize=(15,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quandl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "market = Market(market_data_generator=MarketDataGenerator())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "md_request = MarketDataRequest(\n",
    "        start_date=\"decade\",            # start date\n",
    "        data_source='quandl',     # use Bloomberg as data source\n",
    "        tickers=['APPL'], # ticker (findatapy)\n",
    "        fields=['close'],               # which fields to download\n",
    "        vendor_tickers=['APPL'],   # ticker (Alpha Vantage)\n",
    "        vendor_fields=['Close'])        # which Bloomberg fields to download)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-11-02 14:12:24,753 - findatapy.market.datavendorweb - INFO - Request Quandl data\n",
      "2019-11-02 14:12:24,754 - findatapy.market.datavendorweb - ERROR - The tickers ['APPL'] do not exist on Quandl.\n",
      "'NoneType' object has no attribute 'index'\n",
      "2019-11-02 14:12:24,778 - findatapy.market.marketdatagenerator - WARNING - No data returned for ['APPL']\n"
     ]
    }
   ],
   "source": [
    "df = market.fetch_market(md_request)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NAURU_CURRENT_ACCOUNT_BALANCE.close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>-13.479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>-9.486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-12-31</td>\n",
       "      <td>1.711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-31</td>\n",
       "      <td>4.120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>-7.692</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            NAURU_CURRENT_ACCOUNT_BALANCE.close\n",
       "Date                                           \n",
       "2014-12-31                              -13.479\n",
       "2015-12-31                               -9.486\n",
       "2016-12-31                                1.711\n",
       "2017-12-31                                4.120\n",
       "2018-12-31                               -7.692"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# boe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "boe_url = \"http://www.bankofengland.co.uk/boeapps/iadb/fromshowcolumns.asp?csv.x=yes&Datefrom={start_date}&Dateto={end_date}&SeriesCodes={tickers}&CSVF=TN&UsingCodes=Y&VPD=Y&VFD=N\"\n",
    "start_time = datetime.date(2010, 1, 7).strftime(\"%d/%b/%Y\")\n",
    "end_time = datetime.date(2019, 10, 22).strftime(\"%d/%b/%Y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>IUMBV34</th>\n",
       "      <th>IUMBV37</th>\n",
       "      <th>IUMBV42</th>\n",
       "      <th>IUMBV45</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>31 Jan 2010</td>\n",
       "      <td>3.97</td>\n",
       "      <td>4.64</td>\n",
       "      <td>5.56</td>\n",
       "      <td>..</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>28 Feb 2010</td>\n",
       "      <td>3.88</td>\n",
       "      <td>4.68</td>\n",
       "      <td>5.50</td>\n",
       "      <td>..</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>31 Mar 2010</td>\n",
       "      <td>3.92</td>\n",
       "      <td>4.68</td>\n",
       "      <td>5.50</td>\n",
       "      <td>..</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>30 Apr 2010</td>\n",
       "      <td>3.82</td>\n",
       "      <td>4.65</td>\n",
       "      <td>5.50</td>\n",
       "      <td>..</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>31 May 2010</td>\n",
       "      <td>3.78</td>\n",
       "      <td>4.56</td>\n",
       "      <td>5.38</td>\n",
       "      <td>..</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>112</td>\n",
       "      <td>31 May 2019</td>\n",
       "      <td>1.65</td>\n",
       "      <td>1.79</td>\n",
       "      <td>1.99</td>\n",
       "      <td>2.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>113</td>\n",
       "      <td>30 Jun 2019</td>\n",
       "      <td>1.64</td>\n",
       "      <td>1.77</td>\n",
       "      <td>1.97</td>\n",
       "      <td>2.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>114</td>\n",
       "      <td>31 Jul 2019</td>\n",
       "      <td>1.63</td>\n",
       "      <td>1.76</td>\n",
       "      <td>1.94</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>115</td>\n",
       "      <td>31 Aug 2019</td>\n",
       "      <td>1.61</td>\n",
       "      <td>1.74</td>\n",
       "      <td>1.90</td>\n",
       "      <td>2.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>116</td>\n",
       "      <td>30 Sep 2019</td>\n",
       "      <td>1.56</td>\n",
       "      <td>1.66</td>\n",
       "      <td>1.80</td>\n",
       "      <td>2.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>117 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            DATE  IUMBV34  IUMBV37  IUMBV42 IUMBV45\n",
       "0    31 Jan 2010     3.97     4.64     5.56      ..\n",
       "1    28 Feb 2010     3.88     4.68     5.50      ..\n",
       "2    31 Mar 2010     3.92     4.68     5.50      ..\n",
       "3    30 Apr 2010     3.82     4.65     5.50      ..\n",
       "4    31 May 2010     3.78     4.56     5.38      ..\n",
       "..           ...      ...      ...      ...     ...\n",
       "112  31 May 2019     1.65     1.79     1.99    2.57\n",
       "113  30 Jun 2019     1.64     1.77     1.97    2.59\n",
       "114  31 Jul 2019     1.63     1.76     1.94     2.6\n",
       "115  31 Aug 2019     1.61     1.74     1.90    2.62\n",
       "116  30 Sep 2019     1.56     1.66     1.80    2.65\n",
       "\n",
       "[117 rows x 5 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.read_csv(boe_url.format(start_date=start_time, end_date=end_time,tickers='IUMBV34,IUMBV37,IUMBV42,IUMBV45'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%load_ext autoreload\n",
    "%autoreload 2\n",
    "from findatapy.market.datavendorweb import DataVendorYahoo, DataVendorBOE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "md_request = MarketDataRequest(\n",
    "        start_date=\"decade\",            # start date\n",
    "        data_source='boe',     # use Bloomberg as data source\n",
    "        tickers=['IUMBV34', 'IUMBV37'], # ticker (findatapy)\n",
    "        fields=['close'],               # which fields to download\n",
    "        vendor_tickers=['IUMBV34', 'IUMBV37'],   # ticker (Alpha Vantage)\n",
    "        vendor_fields=['Close'])        # which Bloomberg fields to download)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "boe = DataVendorBOE()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-11-02 14:07:43,529 - findatapy.market.datavendorweb - INFO - Request BOE data\n",
      "2019-11-02 14:07:44,722 - findatapy.market.datavendorweb - INFO - Completed request from BOE.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IUMBV34.close</th>\n",
       "      <th>IUMBV37.close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>30 Nov 2009</td>\n",
       "      <td>4.10</td>\n",
       "      <td>4.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31 Dec 2009</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31 Jan 2010</td>\n",
       "      <td>3.97</td>\n",
       "      <td>4.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28 Feb 2010</td>\n",
       "      <td>3.88</td>\n",
       "      <td>4.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31 Mar 2010</td>\n",
       "      <td>3.92</td>\n",
       "      <td>4.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31 May 2019</td>\n",
       "      <td>1.65</td>\n",
       "      <td>1.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30 Jun 2019</td>\n",
       "      <td>1.64</td>\n",
       "      <td>1.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31 Jul 2019</td>\n",
       "      <td>1.63</td>\n",
       "      <td>1.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31 Aug 2019</td>\n",
       "      <td>1.61</td>\n",
       "      <td>1.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30 Sep 2019</td>\n",
       "      <td>1.56</td>\n",
       "      <td>1.66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>119 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             IUMBV34.close  IUMBV37.close\n",
       "Date                                     \n",
       "30 Nov 2009           4.10           4.88\n",
       "31 Dec 2009           4.05           4.84\n",
       "31 Jan 2010           3.97           4.64\n",
       "28 Feb 2010           3.88           4.68\n",
       "31 Mar 2010           3.92           4.68\n",
       "...                    ...            ...\n",
       "31 May 2019           1.65           1.79\n",
       "30 Jun 2019           1.64           1.77\n",
       "31 Jul 2019           1.63           1.76\n",
       "31 Aug 2019           1.61           1.74\n",
       "30 Sep 2019           1.56           1.66\n",
       "\n",
       "[119 rows x 2 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boe.load_ticker(md_request)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Close', 'Close', 'Close', 'Close', 'Close']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "md_request.vendor_fields * 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ONS "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests, json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "payload = {\n",
    "    \"dataset\": {\n",
    "        \"id\": \"cpih01\",\n",
    "        \"edition\": \"time-series\",\n",
    "        \"version\": \"6\"\n",
    "    },\n",
    "    \"dimensions\": [\n",
    "        {\n",
    "            \"name\": \"geography\",\n",
    "            \"options\": [\n",
    "                \"K02000001\"\n",
    "            ]\n",
    "        }\n",
    "    ]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_url = \"https://api.beta.ons.gov.uk/v1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [404]>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "requests.get(base_url, params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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